Back

Trabectedin in the Treatment of Soft Tissue Sarcoma: Real-world Data on Effectiveness, Safety, and Financial Implications from a European Comprehensive Cancer Centre

Giraud, J.-S.; Watson, S.; Acramel, A.; Laurence, V.; Tzanis, D.; Bonvalot, S.; El Zein, S.; Nicolas, N.; Cros, C.; Desmaris, R.; Bonnet, C.

2025-08-27 oncology
10.1101/2025.08.22.25334220 medRxiv
Show abstract

BackgroundSoft tissue sarcomas (STS) comprise over 150 histological subtypes, with advanced cases showing poor prognosis (5-year survival <10%). Trabectedin, a synthetic alkaloid, is frequently used after anthracycline-based chemotherapy failure. Despite the withdrawal of reimbursement in France in 2018 due to debated efficacy and safety, it remains in clinical use, imposing financial strain on hospitals. MethodsThis retrospective single-center study evaluated trabectedins efficacy, safety, and cost in 68 patients treated between 2019 and 2023. ResultsL-sarcomas accounted for 78% of cases, including uterine leiomyosarcomas (n=16), soft-tissue leiomyosarcomas (n=17), and myxoid liposarcomas (n=8). Non-L-sarcomas (22%) included mostly synovial sarcomas. The overall disease control rate was 71%, with a median progression-free survival (PFS) of 4.1 months. Subtype-specific median PFS was 6.8 months for liposarcomas (11.3 for myxoid vs. 4.5 for other subtypes), 3.1 months for leiomyosarcomas (3.4 months for uterine vs 3.1 for soft-tissue), and 2.4 months for non-L-sarcomas. Patients received a median of 5 cycles (range: 1-38), with an average total dose of 16 mg [2 - 81], and an average hospital cost of {euro}9,900. Adverse events occurred in 91%, mainly hematological; cardiac toxicity was seen in 9%. ConclusionDespite limited reimbursement, trabectedin remains a relevant treatment, particularly in L-sarcoma management.

Published in International Journal of Cancer (predicted rank #13) · training set

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.